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SUMMARY:Cell detection by functional inverse diffusion and group sparsity 
 - Pol del Aguila Pla (KTH - Royal Institute of Technology )
DTSTART:20171102T154000Z
DTEND:20171102T160000Z
UID:TALK94366@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Biological assays in which particles generated by cells bind t
 o a surface and can be imaged to reveal the cells&#39\; location are ubiqu
 itous in biochemical\, pharmacological and medical research. In this talk\
 , I will describe the physics of these processes\, a 3D radiation-diffusio
 n-adsorption-desorption partial differential equation\, and present our no
 vel parametrization of its solution (i.e.\, the observation model) in term
 s of convolutional operators. Then\, I will present our proposal to invert
  this observation model through a functional optimization problem with gro
 up-sparsity regularization and explain the reasoning behind this choice of
  regularizer. I will also present the results needed to derive the acceler
 ated proximal gradient algorithm for this problem\, and justify why we cho
 se to formulate the algorithm in the original function spaces where our ob
 servation model operates. Finally\, I will briefly comment on our choice o
 f discretization\, and show the final performance of our algorithm in both
  synthetic and real data.  <span> arXiv preprints: <a target="_blank" rel=
 "nofollow" href="https://arxiv.org/abs/1710.01604"> arXiv:1710.0164 </a>\,
  <a target="_blank" rel="nofollow" href="https://arxiv.org/abs/1710.01622"
 > arXiv:1710.01622 </a> </span>
LOCATION:Seminar Room 1\, Newton Institute
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